• Journal of the Korean Magnetic Resonance Society
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• v.24 no.2
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• pp.59-65
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• 2020

Nitrogen-Vacancy (NV) center in diamond has been an emerging versatile tool for quantum sensing applications. Amongst various applications, nano-scale nuclear magnetic resonance (NMR) using a single or ensemble NV centers has demonstrated promising results, opening possibility of a single molecule NMR for its chemical structural studies or multi-nuclear spin spectroscopy for quantum information science. However, there is a key challenge, which limited the spectral resolution of NMR detection using NV centers; the interrogation duration for NV-NMR detection technique has been limited by the NV sensor spin lifetime (T₁ ~ 3ms), which is orders of magnitude shorter than the coherence times of nuclear spins in bulk liquid samples (T₂ ~ 1s) or intrinsic ¹³C nuclear spins in diamond. Recent studies have shown that quantum memory technique or synchronized readout detection technique can further narrow down the spectral linewidth of NMR signal. In this short review paper, we overview basic concepts of nanoscale NMR using NV centers, and introduce further developments in high spectral resolution NV NMR studies.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.381-382
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• 2007

In this paper, crack detection technique of concrete bridge is proposed robust against shadow and noise. Our technique consists of two steps. In the first step, crack candidate region is detected by preprocessing. Preprocessing techniques such as median filter, isolated point filter and morphological techniques, use utilized for detection of crack candidate regions. In the final step, crack is detected from crack candidate region by considering any connectivity between cracks. By experimental results, performance is improved 6.8% over the existing method.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.107-114
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• 2023

The attack technique by the malware distribution form is a dangerous, difficult to detect and prevent attack method. Current malware detection studies and proposals are often based on two main methods: using sign sets and analyzing abnormal behaviors using machine learning or deep learning techniques. This paper will propose a method to detect malware on Endpoints based on Event IDs using deep learning. Event IDs are behaviors of malware tracked and collected on Endpoints' operating system kernel. The malware detection proposal based on Event IDs is a new research approach that has not been studied and proposed much. To achieve this purpose, this paper proposes to combine different data mining methods and deep learning algorithms. The data mining process is presented in detail in section 2 of the paper.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.6
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• pp.357-366
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• 2013

Spectrum sensing, as a fundamental functionality of Cognitive Radio (CR), enables Secondary Users (SUs) to monitor the spectrum and detect spectrum holes that could be used. Recently, the security issues of Cognitive Radio Networks (CRNs) have attracted increasing research attention. As one of the attacks against CRNs, a Primary User Emulation (PUE) attack compromises the spectrum sensing of CR, where an attacker monopolizes the spectrum holes by impersonating the Primary User (PU) to prevent SUs from accessing the idle frequency bands. Energy detection is often used to sense the spectrum in CRNs, but the presence of PUE attack has not been considered. This study examined the effect of PUE attack on the performance of energy detection-based spectrum sensing technique. In the proposed protocol, the stationary helper nodes (HNs) are deployed in multiple stages and distributed over the coverage area of the PUs to deliver spectrum status information to the next stage of HNs and to SUs. On the other hand, the first stage of HNs is also responsible for inferring the existence of the PU based on the energy detection technique. In addition, this system provides the detection threshold under the constraints imposed on the probabilities of a miss detection and false alarm.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.592-595
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• 2009

In this paper, we propose a face detection technique for still pictures which sequentially uses a skin-color model and a support vector machine (SVM). SVM is a learning algorithm for solving the classification problem. Some studies on face detection have reported superior results of SVM over neural networks. The SVM method searches for a face in a picture while changing the size of the window. The detection accuracy and the processing time of SVM vary largely depending on the complexity of the background of the picture or the size of the face. Therefore, we apply a face candidate area detection method using a skin-color model as a preprocessing technique. We compared the method using SVM alone with that of the proposed method in respect to face detection accuracy and processing time. As a result, the proposed method showed improved processing time while maintaining a high recognition rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.2
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• pp.371-384
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• 2001

In these days, it is continuously increased to the intrusion of system in internet environment. The methods of intrusion detection can be largely classified into anomaly detection and misuse detection. The former uses statistical methods, features selection method in order to detect intrusion, the latter uses conditional probability, expert system, state transition analysis, pattern matching. The existing studies for IDS(intrusion detection system) use combined methods. In this paper, we propose a new intrusion detection algorithm combined both state transition analysis and association mining techniques. For the intrusion detection, the first step is generated state table for transmitted commands through the network. This method is similar to the existing state transition analysis. The next step is decided yes or no for intrusion using the association mining technique. According to this processing steps, we present the automated generation algorithm of the penetration scenarios.

• Structural Engineering and Mechanics
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• v.80 no.5
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• pp.563-583
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• 2021

Composite materials, composed of multiple constituent materials with dissimilar properties, are actively adopted in a wide range of industrial sectors due to their remarkable strength-to-weight and stiffness-to-weight ratio. Nevertheless, the failure mechanism of composite materials is highly complicated due to their sophisticated microstructure, making it much harder to predict their residual material lives in real life applications. A promising solution for this safety issue is structural damage detection. In the present paper, damage detection of composite material via electrical resistance-based technique and infrared thermography is reviewed. The operating principles of the two damage detection methodologies are introduced, and some research advances of each techniques are covered. The advancement of IR thermography-based non-destructive technique (NDT) including optical thermography, laser thermography and eddy current thermography will be reported, as well as the electrical impedance tomography (EIT) which is a technology increasingly drawing attentions in the field of electrical resistance-based damage detection. A brief comparison of the two methodologies based on each of their strengths and limitations is carried out, and a recent research update regarding the coupling of the two techniques for improved damage detection in composite materials will be discussed.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.176-178
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• 2005

In the images of a video camera employed for surveillance, detecting targets by extracting foreground image is of great importance. The foreground regions detected, however, include not only moving targets but also their shadows. This paper presents a novel technique to detect shadow pixels in the foreground image of a video camera. The image characteristics of video cameras employed, a web-cam and a CCD, are first analysed in the HSV color space and a pixel-level shadow detection technique is proposed based on the analysis. Compared with existing techniques where unified criteria are used to all pixels, the proposed technique determines shadow pixels utilizing a fact that the effect of shadowing to each pixel is different depending on its brightness in background image. Such an approach can accommodate local features in an image and hold consistent performance even in changing environment. In experiments targeting pedestrians, the proposed technique showed better results compared with an existing technique.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.6
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• pp.30-37
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• 1997

We present an efficient technique, bidirectioanl inertial maximum cost search technique, for th edetection and segmentation of circular shaped objects using the spatial information around the neighborhood of the boundary candidates. This technique searches boundary candidates using local pixdl information such as pixel value and its direction. And then to exclude pseudo-boundary caused by shadows or noises, the local contrast is defined between the clique of the boundary candidates and the cliques of the background. In order to effectively segment circular shaped boundary, the technique also uses the curvature based on trigonometirc function which determines circular shaped boundary segments. Since the proposed technique is applied to the pixel cliques instead of a pixel itself, it is proposed method can easily find out circular boundaries form iamges of the PCB containing circular shaped parts and the trees with round fruits compared to boundary detection by using the pixel information and the laplacian curvature.

• Fire Science and Engineering
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• v.26 no.6
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• pp.24-30
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• 2012

This study is on the method of the detection for flame and series arc which can be happened at poor contact point added a vibration in part of contact point of low voltage line. In general, the causes of electric fire are over current, short circuit, poor contact, ect. The over-current or short circuit among those causes is detected by measuring a instant current value, but poor contact is difficult to detect by measuring a excessive value of the voltage and current and a distortion of waveforms. And therefore, in this paper, it is studied on the optimal technique of the arc judgement using fuzzy logic and MDET (Multi Dimension Estimation Technique). And it carries out the simulation for arc detection and the experiment for controller and load test. In result, the controller and detection algoristhm, is classified with normal wave and abnormal arc wave without relation with each loads and so the controller can detect a series arc successfully.